The impact of intangibles on firm growth

This article was downloaded by: [Universita Studi di Pavia], [FRANCESCO SOTTI] On: 01 October 2014, At: 02:18 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Technology Analysis & Strategic Management Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/ctas20

The impact of intangibles on firm growth a

a

a

Stefano Denicolai , Enrico Cotta Ramusino & Francesco Sotti a

University of Pavia, Pavia, Italy Published online: 26 Sep 2014.

To cite this article: Stefano Denicolai, Enrico Cotta Ramusino & Francesco Sotti (2014): The impact of intangibles on firm growth, Technology Analysis & Strategic Management, DOI: 10.1080/09537325.2014.959484 To link to this article: http://dx.doi.org/10.1080/09537325.2014.959484

PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Technology Analysis & Strategic Management, 2014 http://dx.doi.org/10.1080/09537325.2014.959484

Downloaded by [Universita Studi di Pavia], [FRANCESCO SOTTI] at 02:18 01 October 2014

The impact of intangibles on firm growth Stefano Denicolai ∗ , Enrico Cotta Ramusino and Francesco Sotti University of Pavia, Pavia, Italy

This study investigates and measures the impact of intangibles on firm growth. We distinguish between internally and externally generated intangible assets and analyse the role played by firm size, measuring if it can alter the relationship between intangibles and performance. In doing so, we combine the resource-based view of the firm – as a cornerstone for this survey – with accounting principles. In particular, we focus on intangible ‘assets’ recorded in firms’ books according to international accounting standards. The empirical analysis explores a proprietary database of 294 listed companies headquartered in Europe. Findings confirm that intangibles are crucial in fostering firm performance, show that this effect varies with firm size and that an additional boost is created by externally generated intangibles. Keywords: size

1.

resource-based view; intangible assets; externally generated intangible; firm

Introduction: how much intangibles really matter?

In the contemporary economy, competition is more and more driven by the development and accumulation of intangible assets – such as technological knowledge, brand, reputation, and customer base – crucial resources that may make competitive advantage unique and inimitable. Interestingly, companies are increasingly considering a wider spectrum of sources in order to increase their portfolio of resources: as internal development seems to be no longer enough, technology sourcing and other inbound flows of intangibles have become key practices for managers. Mainstream literature recognises the relevance of current developments: a plethora of scholars have explored the topic by discussing why and how both internally and externally generated intangibles feed the competitive advantage of firms (Hall 1992; Peteraf 1993; Teece 1998). Surprisingly, there are few surveys aimed at measuring these dynamics, or – when available – they are too focused on knowledge assets only (e.g. patents), mostly ignoring other important types of intangibles. Furthermore, quantitative surveys in the field are often affected by response bias due to the adoption of subjective metrics such as Likert scales (Bontis 2001; Chareonsuk and Chansa-ngavej 2010). Even though the strategic relevance of intangibles is confirmed by a number of theoretical arguments, we know only a little regarding how much this kind of resource really matters, and under what conditions this effect might be better exploited. ∗ Corresponding

author. Email: [email protected]

© 2014 Taylor & Francis


2

S. Denicolai et al.

According to this premise, this study investigates and measures the role of intangible assets on firm performance through a quantitative approach, distinguishing between internally and externally generated assets. This survey also analyses the role played by firm size, measuring if and how it can alter the relationship between intangibles and performance. Hall (1992) distinguishes between assets – such as trademarks, patents, copyrights, and registered designs – which meet the criteria of accounting rationale, and skills, or competencies – such as tacit knowledge or corporate culture. This paper focuses on the first type, namely those intangible ‘assets’ which are entered in the accounting records of firms according to international accounting principles. We recognise that this choice has some limitations: only a subset of these investments can be capitalised, while ‘soft’ resources are also relevant for competitive advantage. Nevertheless – as mentioned above – the literature in the field shows not only a large consensus from a theoretical standpoint, but also a poor empirical validation regarding its key assumptions, due to a scarcity of quantitative analyses and some controversial findings. We need surveys focused on specific aspects and rooted on objective measurements to make a step forward. Moreover, we think that the integration of strategic management pillars and accounting metrics into the same theoretical framework supports a better understanding of managerial implications, thus reducing the distance between academics and practitioners. The pros and cons of this approach are discussed later. The empirical analysis explores a sample of 294 listed firms headquartered in the UK, Germany, France, and Italy. The need for a distinction between internally and externally generated intangibles led us to collect primary data and develop a proprietary database due to the absence of such information from available sources such as Facsets, Orbis, and Community Innovation Survey (CIS). Our analysis is structured as follows: In Section 2, we introduce the theoretical framework as well as four research hypotheses regarding the role of internally and externally generated intangibles in affecting firm growth. We also introduce firm’s size as a moderating variable. Section 3 describes the sample, the data-set, the definition of firm performance, and explanatory variables. In this section, we motivate the adoption of accounting measures, comparing our approach with those prevailing in existing managerial studies. Section 4 shows the main findings of empirical analysis and discusses the research propositions. In Section 5, we conclude, drawing some theoretical and managerial implications from our work and highlighting possible extensions of this study. 2.

Theoretical framework and hypothesis development: intangible assets, external sourcing, and the role of firm size

The importance of intangible resources has been underlined by several authors (Gerpott, Thomas, and Hoffmann 2008; Hussi and Ahonen 2002). Nevertheless, academic scholars are affected, first, by the absence of a shared and accepted definition of ‘intangible asset’ (Andriessen 2004; Bontis et al. 1999; Mølbjerg-Jørgensen 2006; Sveiby 1997) and, second, by the adoption of heterogeneous taxonomies used by both researchers and practitioners (Bontis 2001). The divergent research path followed by the strategic management stream on one side and accounting literature on the other has a role in explaining the lack of common terminology. This study aims at integrating these two areas of research. To achieve this objective, we define ‘Intangible asset’ – for the sake of brevity, also Intangibles – as a non-financial resource without physical substance that supports both primary and support activities of the firm, potentially enhancing its core-competences and competitive advantage

Impact of intangibles on firm growth

3

(Blair and Wallman 2001; Epstein and Mirza 2005; Onyeiwu 2003). This definition highlights their strategic role and is consistent with international accounting standards.1 The remaining part of this section develops four research hypotheses concerning the role of this type of resource in affecting the firm growth.


2.1.

Intangible assets as a driver of competitive advantage

The resource based view (RBV) stream holds that differences in firm performance are basically the consequence of differences in a firm’s endowment of resources, especially intangible ones (Rumelt 1984). It stresses the importance of internal firm-specific factors, more than industryspecific ones. Strategic resources support the organisation in developing sustainable advantage since they are rare, if not unique (Barney 1991; Dierickx and Cool 1989). They foster the appropriability of competitive advantage since they are difficult to imitate and hard to substitute through other resources (Brown and Kimbrough 2011; Teece 2006). Intangible assets boost firm performance, especially in the medium-long run (Edvinsson and Malone 1997). Indeed, the development and accumulation of such resources require time. Second, while investment in physical assets often shows diminishing marginal returns, investment in intangible assets and human resources is characterised by increasing returns over time (Onyeiwu 2003). Furthermore, intangible assets support the appropriability of the competitive advantage (Jacobides, Knudsen, and Augier 2006). After the seminal articles in the RBV stream, further studies developed the topic. In the last decades, a large number of scholars have confirmed the positive role of intangibles as a critical success factor, especially by focusing on a specific type of assets, such as Brand (O’Cass and Weerawardena 2010; Urde 2009), Knowledge (Blumentritt and Johnston 1999; Teece 1998), Human resources (Wright, Dunford, and Snell 2001), Organisational routines (Becker 2004; Leonardbarton 1992), and Customer base (Coltman 2007; Rapp, Trainor, and Agnihotri 2010). The majority of extant research in the field consists of conceptual articles (Chareonsuk and Chansa-ngavej 2008; Kristandl and Bontis 2007; Teece 1998) or case study analyses (Hall 1993; Huarng and Yu 2011; Stanworth et al. 2004), while we need more quantitative studies to measure and validate the positive role of intangibles (Pike, Roos, and Marr 2005). Our survey addresses this research gap. The remaining part of this section discusses the outcomes of prior quantitative surveys and draws four research hypotheses that will be empirically tested in Section 4. In line with the aim of this study, the theoretical framework relies on the strategic management view (Hall 1992; Rumelt 1984; Rumelt, Schendel, and Teece 1991). As mentioned above, only a limited number of quantitative surveys explicitly address the impact of intangible assets on firm performance. The surveys of Chareonsuk and Chansa-Ngavej (2008, 2010) confirm the positive relationship and add that, to sustain long-term growth, intangibles must be carefully monitored. It highlights the need for accounting practices in enabling the strategic potential of this kind of asset. However, empirical outcomes come from subjective opinions collected through questionnaires. Onyeiwu (2003) studied a sample of 50 companies through a binary-logit estimation procedure. Findings suggest that companies which spend more on intangible assets and marketing activities develop superior core competencies. Nevertheless, the author finds controversial outcomes in the case of some kinds of intangibles, such as technological knowledge (e.g. patents). Chen et al. (2005) used data drawn from Taiwanese listed companies and Pulic’s Value Added Intellectual Coefficient (VAICe) as the efficiency measure of intellectual capital. This survey


4

S. Denicolai et al.

shows that this component of the intangibles’ portfolio has a positive impact on market value and financial performance, but does not focus on the sources of intangible assets. Empirical analyses have also suggested that companies with a high portion of intangibles to total assets – in contrast with capital- and labour-based firms – show a relatively better ability to cope with the turbulence of the contemporary scenario (Bell, Crick, and Young 2004; Powell and Snellman 2004). The rise of intangible assets in size and contribution to corporate growth over the last decades is also noticed in companies’ annual reports (Gu and Wang 2005). Hence, according to this literature review, we first posit the following research hypothesis: Hypothesis 1: Intangible Intensity is positively associated with firm performance.

We focus on a ‘ratio’ measure, more than on stocks. The aim is to investigate the structure of the resource portfolio, and to mitigate the effect of firm size (Carmeli and Tishler 2004; Denicolai et al. 2014; Srivastava 2014). Thus we define ‘Intangible Intensity’ as the total value of Intangibles to the total value of non-current assets. The focus on ‘non-current assets’ makes it possible to consider only strategic assets which are held by firms in the medium and long run. 2.2.

Internally vs. externally generated intangibles

The literature also emphasises the difference between internally and externally generated intangibles (Stolwijk et al. 2012). The global economy and turbulent technological evolution have pushed towards the decentralisation of knowledge sourcing, and the diversification of the brand portfolio. As such, this strategic pathway cannot be fully internally pursued due to its complexity and the huge related investments. Thus, companies open up their business model and consider the option to access and internalise intangibles developed by independent organisations (Chesbrough 2003; Huang 2011). This solution offers a number of advantages. The company usually acquires an external intangible when the latter has already expressed its potential, or if its value is confirmed by evidence, thus reducing the risk of the investment (Granstrand et al. 1992). For the same reason, by leveraging externally generated intangibles, the company reduces the time lag between investment (expenditure) and returns. This orientation is more and more widespread and takes place in many ways: strategic alliances, business acquisitions aimed at accessing a strategic asset (e.g. a well-known trademark), direct acquisition of a specific asset through a market transaction (e.g. a patent), licensing agreement, and so on. Prior research confirmed a relationship among external intangibles – especially knowledge – innovation and firm performance (Jones, Lanctot, and Teegen 2001; Taylor and Lowe 1997; Tsai and Wang 2007). Nevertheless, some scholars argue that the topic is quite controversial and call for further investigation (Stolwijk et al. 2012). Hence, we posit a second research hypothesis as follows: Hypothesis 2: The orientation towards externally generated intangibles is positively associated with firm performance.

2.3.

The impact of intangibles and firm size

The literature also investigates whether intangibles are sensitive to organisational conditions, which may enhance or mitigate their effect on performance (Tsai and Wang 2007; Vega-Jurado, Gutierrez-Gracia, and Fernandez-De-Lucio 2009). Prior research discussed a number of variables



5

which can moderate the relationship between intangibles and performance, such as innovation capability (Gomez and Vargas 2012), technology life cycle (Stolwijk et al. 2012), tangible resources and technological slack (Bueno et al. 2010), foreign direct investment (Jiang et al. 2011), and corporate responsibility (Surroca, Tribo, and Waddock 2010), firm age (Dehlen et al. 2014), strategic orientation (Hagen et al. 2012; Troilo, De Luca, and Atuahene-Gima 2014), top management traits and behaviour (Li et al. 2011), etc. In particular, the literature highlights the positive role of absorptive capacity, which positively moderates the decision to acquire external intangibles, especially when the motivation to acquire is low, or financial resources are limited (Ruth, Iyer, and Sharp 2013). Surprisingly, we have not found surveys aimed at investigating firm size as a moderating variable; in extant research, size is considered just as a control variable, not as a key driver (Chareonsuk and Chansa-Ngavej 2010). We surmise that firm size is a fundamental aspect in moderating the relationship between intangible intensity and firm performance. SMEs are expected to be more sensitive to the role of intangibles because of the relative weight of critical assets in their business life cycle. Larger organisations also benefit from intangible assets, but their performance tends to depend on a broader spectrum of factors (Elsayed 2006; Gopalakrishnan and Bierly 2006). Thus, we investigate if the effect of intangible intensity on firm performance varies according to firm size. Prior research shows controversial findings regarding this standpoint. Some scholars argue that larger firms can take advantage of economies of scale and therefore more effectively turn intangible assets into competitive advantage (Jiang et al. 2011). By contrast, a large body of literature neglects this assumption and conversely posits that small enterprises are more able to exploit the ‘boost effect’ derived from intangible assets. The typical high-potential SME develops its competitive advantage on high-value intangible resources, while often it is poor in terms of physical and financial assets (Maranto-Vargas and Rangel 2007; Thorpe et al. 2005). By contrast, on average, the large-sized company shows a more diversified portfolio of resources. Moreover, the boost produced by intangibles tends to be more evident in young and small firms since – because of their state – they have more chance to grow, and faster, than the already established big companies. We believe that a better understanding of this aspect could reveal relevant implications for both managers and policy-makers. Thus, we posit the following hypothesis: Hypothesis 3a: Firm size negatively moderates the impact of intangible intensity on firm performance.

Firm size could, in principle, even affect the successful acquisition of external assets. In other words, we surmise that the impact of externally generated intangibles on firm performance varies across small, medium, and large firms. In particular, small and medium enterprises may have limited resources for technology acquisitions, but they are relatively more likely to benefit from externally generated intangibles when this happens (Anderson and Eshima 2013). Successful SMEs counterbalance their lack of financial resources through superior capabilities and flexibility in searching/introducing intangibles from outside (Kotlar et al. 2013). It is a sort of ‘survival need’ which can make the difference between successful companies and organisations that stay small, or fail (Cho and Yu 2000; Il Park and Ghauri 2011). Big companies, on the contrary, show relatively more capacity in terms of internal development and are less dependent on external players (Hennart 1991). Moreover, large organisations tend to be more path-dependent to internal processes (Nerkar and Paruchuri 2005) as well as more affected by the so-called Not Invented

6

S. Denicolai et al. CONTROL VARIABLES a) Industry b) Country c) Firm Size


PERFORMANCE

(firm growth 2010-2013)

INTANGIBLE INTENSITY

EXTERNALLY GENERATED INTANGIBLE RARIO (EGIR)

FIRM SIZE Figure 1. Research model.

Here Syndrome (Chesbrough and Crowther 2006). Hence, we assume a negative moderating effect, and we posit a final research hypothesis: Hypothesis 3b: Firm size negatively moderates the effect of externally generated intangibles on firm performance.

In a nutshell, the first two hypotheses investigate the impact of Intangibles on firm performance, by distinguishing between the whole portfolio (Hp1) and the specific impact of externally generated assets (Hp2). Hypotheses 3a and 3b study the role of firm size as a moderating variable that might reinforce, or mitigate, these dynamics. Figure 1 summarises the whole research model.

3. 3.1.

Methodology and variables Sample and source of data

We developed a proprietary database composed of secondary data and consistent with the purpose of our survey. This approach differentiates our study from others which rely on information collected through surveys. The latter has, of course, its advantages, since it permits the collection of data which are consistent with any kind of pre-defined research model. On the other side, the reliability of information collected through questionnaires is entirely based on the commitment of responding firms. We decided to gather quantitative data from annual reports, an approach that is both objective, being based on official information, and practical, as it would have been difficult to use questionnaires to a huge amount of precise information from a large sample of firms. We are aware that our approach also has, of course, some limitations as financial reports do not contain information about the whole portfolio of intangibles available for the firm, but only those recorded


7


Table 1. Composition of the sample in terms of countries. Country

No. of companies

Percentage

UK Germany France Italy Total

153 81 54 22 310

49.35 26.13 17.42 7.10 100.00

Table 2. Composition of the sample in terms of industries. Industry Industrial goods and services Technology Health care Other Total

No. of companies

Percentage

126 93 32 59 310

40.65 30.00 10.32 19.03 100.00

Table 3. Composition of the sample in terms of firm size (number of employees). Number of employees Less than 50 51–250 251–2500 More than 2500 Total

No. of companies

Percentage

25 65 112 108 310

8.06 20.97 36.13 34.84 100.00

according to accounting standards. We only looked at European listed companies and consolidated financial statements, all adopting the IAS/IFRS since 2005, thus guaranteeing relevance, materiality, comparability, verifiability, faithfulness, and as a consequence, homogeneous, and consistent data. Data about relevant aspects of intangibles were collected partly from raw figures drawn from the income statement and balance sheet, partly combining these two schemes and partly from the notes of disclosure. We decided to focus on companies listed in the financial markets of the four major EU economies: UK,2 Germany,3 France,4 and Italy,5 representing together more than half of the European Union’s GDP. We first developed a preliminary analysis of all the companies (2178) listed on the mentioned exchanges at the date we started our research (2011), in order to identify those that could be used for our purposes. We then focused on firms with consolidated reports, as these are perfectly comparable among countries. Finally, we selected only companies showing a clear-cut distinction between ‘internally generated intangible assets’ and ‘externally generated intangible assets’. This left us with a sample of 310 companies: 153 in the UK, 54 in France, 81 in Germany, and 22 in Italy. Tables 1–3 show some descriptive statistics. The sample is similar in country composition to the full population of listed companies in the above-mentioned stock

8

S. Denicolai et al.

exchanges. Due to some missing values and outliers, the final sample for regression analysis consists of 294 observations. Though it is a limited sample in absolute terms, it is well representative of European listed firms.


3.2.

Dependent variable

In this section, we describe the variables used in our research model, coherently with our research hypotheses. Turnover growth (GROW) is, in our research scheme, the measure of a firm’s success and the dependent variable in our regression analysis. We measured it, according to IAS 18, §7, through total revenues generated by the group during the fiscal year. In this study, we assume turnover’s compounded average growth rate (CAGR) over the 2008–2010 period as proxy for firm performance. Like other metrics, this option has some limitations. In particular, it can favour SMEs and new ventures which – due to their nature – might have more growth potential due to the fact they start from a smaller base compared with big companies. However, sales growth is a more stable indicator compared with profitability, which shows high volatility and is highly industry-specific. Third, we decided to focus on this indicator coherently with the approach used in a number of managerial studies, where growth is widely referred to as a proxy for firm success (Bruton and Rubanik 2002; Coad 2010). 3.3.

Independent variables

The first independent variable is ‘Intangible Intensity’ (INT), defined as ‘intangible assets’ scaled by ‘total non-current assets’. Total intangible assets is the net (of amortisation and impairment losses) book value of all intangible assets, excluding goodwill, including acquired and internally generated. This value is recorded in the firm’s accounts on the basis of effective requirements derived from IAS 38 (intangible assets) and IFRS 3 (business combinations). In particular, IAS 38 is very strict in defining, identifying, measuring, and recognising intangible assets. Given the lack of physical substance, the IAS/IFRS framework sets precise requirements an asset must meet in order to be recorded in a firm’s balance sheet. According to IAS 38, assets in this category are intangible resources such as scientific or technical knowledge, design and implementation of new processes or systems, licences, intellectual property, market knowledge, and trademarks (including brand names and publishing titles).6 However, an item from this list is defined as an intangible asset and it is recognised in the financial statement if, and only if, it meets the following three criteria: identifiability, control over a resource, and existence of future economic benefits (IAS 38, §10). After being recognised, an intangible asset may be included in the balance sheet if and only if: • it is probable that the expected future economic benefits attributable to the asset will flow to the entity and • the cost of the asset can be measured reliably (IAS 38, §21). Undoubtedly, financial statements analysed provide reliable data and information, but we cannot exclude that there could be intangible resources that are not recognised in the balance sheet even if they are useful for the entity.



9

Total non-current assets include the net book value of all those assets classified in the balance sheet as non-current as they are not expected to be realised in the entity’s normal operating cycle or within 12 months from the end of the reporting period. Assets are measured at the cost of acquisition (or generation) minus accumulated amortisation and depreciation, or at fair value if it can be measured reliably. From the balance sheet, we have drawn the net total accumulated value of intangibles – both internally and externally generated – excluding goodwill, at the end of the fiscal year. As the IAS/IFRS framework does not impose the distinction between the two categories of intangibles, relevant data about this aspect have been collected through the analysis of the notes of disclosure. The second key variable in this study is the externally generated ratio (EXT), measured by the amount of Externally generated intangibles to Internally plus Externally generated intangibles (total intangibles). Internally generated intangible assets derive from internal activities performed by the firm during the year in question. According to IFRS, firms do not recognise internally generated goodwill and other items such as brands, mastheads, publishing titles, or customer lists. IAS 38 recognises the difficulties in identifying whether an internally generated intangible asset meets the criteria for recognition. §51 highlights the problems of assessing an internally generated asset’s future benefits and the problem of reliable cost allocation. The IFRS consequently introduce a set of additional requirements for the recognition and initial measurement of internally generated intangible assets. If the intangible asset fulfils all recognition criteria, its cost, measured as the sum of expenditures incurred from the date when the intangible asset first met all the recognition criteria, will be capitalised. These costs include ‘all directly attributable costs necessary to create, produce, and prepare the asset to be capable of operating in the manner intended by management’ as for instance, cost of materials, costs of employee benefits, cost to register legal rights, amortisation of patents, and licences that are used to generate the intangible asset. In short, internally generated intangibles are the following: licences and franchises, copyrights, patents and other industrial property rights, service and operating rights, recipes, formulae, models, designs, prototypes, and intangible assets under development. (IAS 39, § 119) Externally acquired intangible assets include all acquired intangibles, excluding goodwill, making no difference between the different ways of acquisition (separate acquisition or business combinations). According to IFRS, separately acquired intangible assets by definition satisfy criteria for recognition, based on the assumption that the price paid reflects the expectation of obtaining future benefits and that the cost of obtaining the asset is easily identifiable. Some categories of intangible assets such as customer lists, brands, and similar items may therefore be recognised and included in the balance sheet if acquired in a separate transaction or in a business combination, while this is not the case when they are internally generated. Externally acquired intangible assets thus include know-how assets that accounting principles do not allow to be capitalised when generated internally. The above description is aimed at clarifying that the quantitative approach we followed has, of course, some limitations. First, we must note that accounting principles do not, by definition, give a full picture of the whole stock of intangible resources at work within the firm, for the benefit of the firm. There are ‘soft’ resources which do not appear but are nevertheless relevant to a firm’s success, as in the case of employee commitment and ability, tacit knowledge, reputation, relationships with stakeholders, and, as mentioned above, there are cases in which intangible resources may emerge only as a consequence of external acquisition, while they do not emerge at

10

S. Denicolai et al.


all in companies which do not engage in these transactions. In summary, we adopted an approach based on consistent financial information that allows quantitative analysis based on comparability among firms and a solid framework guaranteed by the international accounting principles framework, aware that other approaches exist, with their pros and cons. We used the logarithm of turnover in 2010 as a measure of firm size (SIZE) in order to investigate the moderating effect between intangibles and firm growth. Finally, we also included some control variables, namely industry dummies and country dummies.

4.

Regression analysis and discussion of findings

Table 4 shows some preliminary data about descriptive statistics and correlations. The Intangible Intensity (INT) is little right-skewed since the majority of companies (88% of the sample) have less than 50% of intangibles in their resources portfolio, but the sample covers almost the full range between 0% and 100%. The orientation towards externally generated intangibles (EXT) is very well balanced: the variable range is fully covered, while the mean is close to the middle (46.77%). The Pearson correlations between the variables are fairly small, thus the likelihood of a multicollinearity problem in the regression analysis is low. An Ordinary Least Squares (OLS) regression using STATA 12 has been developed to investigate the above-defined research hypotheses. Table 5 outlines the results of this analysis. Model 1 includes only control variables, among which we include ‘firm size’ (SIZE), considered in Hypotheses 3a and 3b. It is considered as a moderating variable. Industry and country dummy variables are also included in ‘Model 1’. The explanatory power is low (R2 = 0.065), thus suggesting that sector, country, and size have a secondary role in explaining firm growth for the companies in our sample. In particular, none of the country dummies is statistically significant; nevertheless, we retain them in all subsequent regressions to capture any country-specific effects. ‘Health Care’ is the only significant coefficient among the industry dummies. This is not surprising as it reflects the positive trends in biotech and pharma industries in recent years. Interestingly, even firm size is non-significant and this result partially contrasts with studies which assume that size is a proxy for the resource portfolio owned by a company (Dhanaraj and Beamish 2003). Conversely, the fact that control variables have little merit in explaining firm performance suggests the need for more discussion about the explanatory variables used in verifying our research hypotheses. Model 2 considers the link between Intangible Intensity (INT) and firm performance (GROW). The result is statistically significant and improves the explanatory power of the model

Table 4. Descriptive statistics and correlation coefficients.

TURNOVER (mile) INT EXT GROW

Minimum

Maximum

Mean

Std. dev

17 0.00% 0.00% − 28.85%

109,100 96.92% 100.00% 64.96%

3214 23.79% 46.77% 2.93%

11,638 21.934 35.922 0.143

= log (TURNOVER). *Correlation is significant at the 0.01 level. Note: Italic values refer to correlations among key variables.

a SIZE

SIZEa

INT

− 0.3398* 0.2665* − 0.2339* − 0.1553* 0.2809*

EXT

0.0219


11


Table 5. Regression analysis.

C_UK C_GE C_IT I_IND I_TECH I_HC SIZE INT EXT INT_size _cons No. of obs Prob > F R2 Adj R2 Root MSE Mean VIF

Model 1

Model 2

Model 3

0.0086 (0.725) 0.0017 (0.947) − 0.0495 (0.169) − 0.0267 (0.25) − 0.0031 (0.901) 0.0628 (0.057)* − 0.0141 (0.101)

− 0.0073 (0.761) − 0.01 (0.694) − 0.0554 (0.115) . − 0.012 (0.598) − 0.0098 (0.693) 0.0624 (0.053)* − 0.0074 (0.384) 0.1663 (0.000)***

0.005 (0.843) 0.001 (0.969) − 0.0538 (0.125) − 0.0061 (0.791) − 0.0039 (0.875) 0.0652 (0.043)** − 0.0084 (0.331) 0.1761 (0.000)*** 0.044 (0.079)*

0.1517 (0.072)* 294 0.0068 0.0653 0.0425 0.1408 1.74

0.065 (0.443) 294 0.0000 0.1161 0.0913 0.1372 1.70

0.0361 (0.676) 293 0.0000 0.1258 0.0980 0.1369 1.70

Model 4 0.0131 (0.599) 0.0153 (0.564) − 0.0469 (0.178) − 0.0082 (0.719) − 0.0046 (0.853) 0.0767 (0.017)** 0.0181 (0.159) 0.1113 (0.019)** 0.0435 (0.079)* − 0.0977 (0.006)*** − 0.1836 (0.117) 293 0.0000 0.1488 0.1186 0.1353 2.12

*Regression coefficient is significant at the 10% level. **Regression coefficient is significant at the 5% level. ***Regression coefficient is significant at the 1% level.

(R2 = 0.116). The coefficient of INT is positive, thus supporting Hypothesis 1: Intangible Intensity is positively associated with performance, meaning that – according to our data – having a significant portion of intangible assets within the stock of resources is a positive aspect for firm’s growth. Interestingly, the coefficient of the constant decreases dramatically, thus offering a further confirmation regarding the reliability of the Intangible Intensity variable. We then extended our analysis by considering the orientation towards externally generated intangibles (EXT) in Model 3. The explanatory power is further improved (R2 = 0.126), the new coefficient is strongly significant and positive, and the constant coefficient is decreased by half. These findings support Hypothesis 2 and confirm that a strategy grounded on inbound flows of intangibles – through acquisitions and/or business combinations – is associated with better performance in terms of growth. Interestingly, the coefficient INT grows slightly in Model 3, thus supporting the mutual, positive interplay between intangible intensity and the orientation towards externally generated intangibles in affecting firm performance. In both cases, firm size – verified in terms of its direct impact – emerges as a non-significant element across the regression models. One may think that large organisations are in a better position to gain from externally generated intangibles due to the availability of financial resources, while this option may not be possible for many SMEs (Bayona-Saez, Cruz-Cazares, and Garcia-Marco 2013; Chudnovsky, Lopez, and Pupato 2006). We thus investigated, in Model ‘4’, whether firm size (included in the regression as ‘INT_size’7 ) plays an indirect role, moderating the effect of intangible intensity (both internal and external). Interestingly, the coefficient is significant, negative and supports the further improvement of the model (R2 = 0.149). Thus, Hypothesis 3a is also supported: the intangible boost works differently in SMEs and large-sized companies. Our findings suggest the positive effect of intangibles is amplified in medium and, especially, small firms, while

12

S. Denicolai et al.

Table 6. Average firm growth: INT and firm size. SIZE (turnover)


< 28.7 mil ( < q1) Intangible intensity

HIGH (over mean) LOW (below mean) Total

28.7 mil − 941.3 mil (q1 − q3)

> 941.3 mil ( > q3)

Total

9.51%

6.25%

1.75%

6.92%

6.13%

− 1.11%

1.84%

0.75%

8.28%

1.16%

1.82%

2.93% (whole sample)

this impact decreases as firm size increases. After all, even the internal R&D activity is very expensive, and the acquisition of external intangibles is a relatively cheaper and less risky option. Our findings are consistent with the dominant stream of literature in the field (Chesbrough 2003; D’angelo et al. 2013; Fernhaber, Mcdougall-Covin, and Shepherd 2009; Tsai and Wang 2008). The sample consisting of listed firms suggests that almost all these companies are in a position to pursue some technology acquisitions. The regression analysis concerning Hypothesis 3b does not show a reliable outcome: the coefficient (EXT_size) is statistically non-significant (0.992), while the improvement of the model is almost zero. For this reason, we do not report these results in Table 4. Hypothesis 3b is rejected: firm’s size does not alter the positive impact of externally generated intangibles on firm performance, which tends to remain stable in both SMEs and large-sized firms. Our explanation is twofold. First, both categories of firms are in a position to benefit from the acquisition of external intangibles: while a large firm can leverage superior capabilities and more financial resources (Granstrand et al. 1992), SMEs can be more focused, flexible and speedy in selecting external intangibles and putting them to work (Chesbrough and Crowther 2006; Cho and Yu 2000; Narula 2004). Second, acquiring external intangibles is an explicit investment decision, implying rationality in valuation: the firm, whatever its size, is expected to accurately analyse the value of this investment to efficiently allocate financial resource (Pablo, Sitkin, and Jemison 1996). The coefficients of all explanatory variables remain stable across models, thus reinforcing the reliability of our findings. In Table 6, we provide further descriptive statistics. The cells show the average firm growth in the period 2008–2010 by making a split between low and high degrees of Intangible Intensity, as well as among small, medium, and large-sized companies. Due to the nature of the sample (listed companies), we use the first and third quartile of turnover distribution as thresholds for firm size, instead of the mainstream definitions of SMEs. All combinations among these two variables are reported in the Table. The data confirm the above findings. Finally, Table 7 shows three robustness checks (Columns 2–4), compared to the main set of findings (Column 1). Basically, we repeated the regression analysis using both adjusted and different dependent variables. First, in Column 2 we managed the outliers through the winsorising procedure (Hawkins 1993).8 Second, in Column 3 we transformed the dependent variable GROW into a dummy variable9 and ran a Logit regression. Finally, in Column 4 we introduced a different performance indicator, namely the growth of non-current assets (Asset Growth).10 The results shown in Table 7 overall confirm the reliability of our findings, especially with regards to the positive role of Intangible Intensity and Externally Generated Intangibles.


13


Table 7. Robustness checks. Dependent variable

1. Turnover growth (main indicator)

2. Turnover growth (outliers set to extremes)

3. Turnover growth as dummy variable

4. Asset growth

Type of regression

OLS

OLS

logit

OLS

0.009 (0.021) 0.012 (0.022) − 0.046 (0.029) 0.006 (0.735) 0.017 (0.423) 0.076 (0.006)*** 0.015 (0.149) 0.115 (0.005)*** 0.045 (0.034)** − 0.05 (0.098)* − 0.181 (0.071)* 293 0.0000 0.1272

0.182 (0.387) − 0.082 (0.412) − 1.027 (0.602)* 0.199 (0.584) 0.234 (0.551) 0.987 (0.054)* 0 (0.997) 2.76 (0.000)*** 0.847 (0.031)** 0.258 (0.655) − 1.53 (0.411) 293 0.0007

− 0.012 (0.661) 0 (1) − 0.06 (0.131) 0.017 (0.507) 0.024 (0.376) 0.023 (0.509) 0.024 (0.064)* 0.117 (0.03)** 0.047 (0.087)* − 0.062 (0.093)* − 0.223 (0.063)* 293 0.0205 0.0711

C_UK C_GE C_IT I_IND I_TECH I_HC SIZE INT EXT INT_size _cons No. of obs Prob > F R2 Pseudo R2

5.

0.0131 (0.599) 0.0153 (0.564) − 0.0469 (0.178) − 0.0082 (0.719) − 0.0046 (0.853) 0.0767 (0.017)** 0.0181 (0.159) 0.1113 (0.019)** 0.0435 (0.079)* − 0.0977 (0.006)*** − 0.1836 (0.117) 293 0.0000 0.1488

0.0765

Conclusions

The aim of this study is to measure the impact of intangibles – distinguishing between internally and externally generated ones – on firm performance, the latter measured in terms of growth. The distinction between the two mentioned categories of intangible resources is relevant from a theoretical point of view as it gives an idea of the different alternatives available in the accumulation of crucial assets. Moreover, the assessment of different types of intangibles and the introduction into the research model of a moderating variable – firm size – serve the purpose of making a step forward in the field, considering the controversial findings noticed in extant literature, especially in terms of quantitative evidence. As a consequence, we have been compelled to build a proprietary database, given that in those currently used for empirical analysis, such information is not available. From a theoretical point of view, our contribution may prove interesting as the methodology we used creates a link between managerial studies, focused on the strategic relevance of intangibles in building competitive advantage, and accounting studies, mainly focused on how intangibles must be identified, measured, and reported in financial statements. To this end, we used the IAS/IFRS definition of intangibles – that is, the one used by European listed companies to report their results – aware that this approach has clear advantages and some limitations. On the one side, we have the advantage of relying on objective financial information, a fundamental basis for comparison and quantitative measurement. It makes it possible to address a notable research gap since only a limited number of studies about intangibles have been supported by quantitative surveys and reliable data. On the other, we know that only some categories of intangibles may be recorded in a firm’s account, while others are not coherent with the principles of financial reporting.


14

S. Denicolai et al.

Our findings confirm the theoretical cornerstones of the RBV and introduce novel evidence. First, our results support the idea that intangibles are crucial in fostering firm performance. Second, we find that externally generated intangibles additionally boost performance. Third, our findings show that firm size does not influence per se performance. However, the impact of intangibles on firm growth varies depending on firm size, slowly decreasing as the company grows. Even if intangibles remain crucial for large organisations, it is sensible to assume that they may radically change the future of a small firm, while big companies are relatively more dependent on a higher number of strategic factors, both internal and external. Managerial implications are relevant. Findings suggest that ‘type of assets and composition of portfolio’ are more important that ‘having a large stock of resources’, even though different organisational structures (i.e. small vs. big companies) require different configurations of the resource portfolio (i.e. intangible intensity). In short, no matter how large the asset portfolio is, you must have the strategic resources. The sample period of this study coincides with the global economic and financial crisis; we think that our outcomes are interesting since they further support the positive role of such investments, which boost performance even in hard times. The cornerstones of this study may have limitations. First the focus on listed companies – which are mainly big firms – limits the validity of our findings to this particular kind of organization, whilst a similar survey on unlisted SMEs would be desirable. Second, this investigation is limited to intangible assets which are reported in company financial reports (patents, trademarks and so on). This approach, by construction, excludes other categories of intangible resources which may be relevant for firm performance. Third, longitudinal analyses are needed to confirm the causal relations among the variables. Nevertheless, we think that working on objective data makes it possible to take a step forward in the strategic management field, where the analysis of intangibles is often pursued by using questionable metrics.

Funding Stefano Denicolai gratefully acknowledges financial support from Cariplo Foundation International Recruitment Call: The internationalisation of Italian firms: the role of intangibles, managerial resources, and corporate governance.

Notes 1. According to the ‘Financial Accounting Standards Board Accounting Standard Codification 350 (ASC 350)’, an intangible is an asset, other than a financial asset, that lacks physical substance. The ‘International Accounting Standard 38’ defines an intangible asset as an identifiable non-monetary asset without physical substance. 2. London Stock Exchange plus Alternative Investment Market. 3. Xetra. 4. Euronext Paris. 5. Mercato Telematico Azionario – Italian stock exchange. 6. Common examples of items encompassed by these broad headings are computer software, patents, copyrights, motion picture films, customer lists, mortgage servicing rights, fishing licences, import quotas, franchises, customer or supplier relationships, customer loyalty, market share, and marketing rights (IAS 38, §9). 7. INT_size = INT*log(turnover). 8. According to the winsorising procedure, we calculated the upper and lower limits by adding and subtracting the standard deviation (0.143) to the GROW mean (0.0293). The upper limit is 0.169, while the lower one is − 0.111. We maintained the observations within these thresholds – 221 out 293 observations, meaning the 75.4% of the whole sample – while we replaced with limit values the ones which exceed them, in order to manage the outliers and, at the same time, keep the total number of observations. 9. We used the mean as a threshold between worse performers (set to 0) and best performers (set to 1). 10. ‘Asset growth’ has been operationalised as the log-difference between asset stocks in years 2010 and 2008. The correlation between turnover growth and asset growth is positive, but relatively low (0.2928).


15

Notes on contributors


Stefano Denicolai is Assistant Professor of ‘Innovation Management’ at the University of Pavia (Italy), where he is also Head of Double Degree Programs and International Activities. His research interests include the strategic management of intangibles, the economic optimisation of open innovation dynamics, and the international entrepreneurship. He is author of books and papers in the field, including in journals such as International Business Review, Journal of World Business, and Tourism Management. Enrico Cotta Ramusino is a Full Professor of Economics and Business Management at the Economics Faculty at the University of Pavia. He previously taught at the Universities of Perugia and Insubria (Varese). He is Vice President of the Consortium of Postgraduate Studies in Corporate Economics, in Pavia. He is also a member of the board of directors of the Maria Corti Foundation. He is the administrator of various Banks and Finance companies and has also written numerous scientific publications on Strategy, Finance, and Enterprise Valuation. Francesco Sotti, PhD, is Assistant Professor in Accounting at the University of Pavia. He is also Chartered accountant and Statutory auditor. He is an author of books and paper in the accounting field and concerning the measurement of intangible assets.

References Anderson, B. S., and Y. Eshima. 2013. “The Influence of Firm Age and Intangible Resources on the Relationship Between Entrepreneurial Orientation and Firm Growth Among Japanese Smes.” Journal of Business Venturing 28 (3): 413– 429. Andriessen, D. 2004. Making Sense of Intellectual Capital. Amsterdam: Elsevier. Barney, J. 1991. “Firm Resources and Sustained Competitive Advantage.” Journal of Management 17 (1): 99–120. Bayona-Saez, C., C. Cruz-Cazares, and T. Garcia-Marco. 2013. “Public R&D Funding: Does the Source Determine the Strategy?” Technology Analysis & Strategic Management 25 (2): 235–248. Becker, M. C. 2004. “Organizational Routines: A Review of the Literature.” Industrial and Corporate Change 13 (4): 643–677. Bell, J., D. Crick, and S. Young. 2004. “Small Firm Internationalization and Business Strategy - An Exploratory Study of ‘Knowledge-Intensive’ and ‘Traditional’ Manufacturing Firms in the UK.” International Small Business Journal 22 (1): 23–56. Blair, M. M., and S. M. H. Wallman. 2001. Unseen Wealth – Report of the Brookings Task Force on Intangibles. Washington, DC: The Brookings Institution. Blumentritt, R., and R. Johnston. 1999. “Towards a Strategy for Knowledge Management.” Technology Analysis & Strategic Management 11 (3): 287–300. Bontis, N. 2001. “Assessing Knowledge Assets: A Review of the Models Used to Measure Intellectual Capital.” International Journal of Management Reviews 3 (1): 41–60. Bontis, N., N. Dragonetti, K. Jacobsen, and G. Roos. 1999. “The Knowledge Toolbox: A Review of the Tools Available to Measure and Manage Intangible Resources.” European Management Journal 17 (4): 391–402. Brown, N. C., and M. D. Kimbrough. 2011. “Intangible Investment and the Importance of Firm-Specific Factors in the Determination of Earnings.” Review of Accounting Studies 16 (3): 539–573. Bruton, G. D., and Y. Rubanik. 2002. “Resources of the Firm, Russian High-Technology Startups, and Firm Growth.” Journal of Business Venturing 17 (6): 553–576. Bueno, E., J. A. Aragon, M. P. Salmador, and V. J. Garcia. 2010. “Tangible Slack Versus Intangible Resources: The Influence of Technology Slack and Tacit Knowledge on the Capability of organisational Learning to Generate Innovation and Performance.” International Journal of Technology Management 49 (4): 314–337. Carmeli, A., and A. Tishler. 2004. “The Relationships Between Intangible Organizational Elements and Organizational Performance.” Strategic Management Journal 25 (13): 1257–1278. Chareonsuk, C., and C. Chansa-Ngavej. 2008. “Intangible Asset Management Framework for Long-Term Financial Performance.” Industrial Management & Data Systems 108 (5–6): 812–828. Chareonsuk, C., and C. Chansa-Ngavej. 2010. “Intangible Asset Management Framework: An Empirical Evidence.” Industrial Management & Data Systems 110 (7): 1094–1112. Chen, M.-C., S.-J. Cheng, and Y. Hwang. 2005. “An Empirical Investigation of the Relationship Between Intellectual Capital and Firms’ Market Value and Financial Performance.” Journal of Intellectual Capital 6 (2): 159–176. Chesbrough, H. W. 2003. “The Era of Open Innovation.” MIT Sloan Management Review 44 (3): 35–41.


16

S. Denicolai et al.

Chesbrough, H., and A. K. Crowther. 2006. “Beyond High Tech: Early Adopters of Open Innovation in Other Industries.” R & D Management 36 (3): 229–236. Cho, D. H., and P. I. Yu. 2000. “Influential Factors in the Choice of Technology Acquisition Mode: An Empirical Analysis of Small and Medium Size Firms in the Korean Telecommunication Industry.” Technovation 20 (12): 691–704. Chudnovsky, D., A. Lopez, and G. Pupato. 2006. “Innovation and Productivity in Developing Countries: A Study of Argentine Manufacturing Firms’ Behavior (1992–2001).” Research Policy 35 (2): 266–288. Coad, A. 2010. “Exploring the Processes of Firm Growth: Evidence from a Vector Auto-Regression.” Industrial and Corporate Change 19 (6): 1677–1703. Coltman, T. 2007. “Why Build a Customer Relationship Management Capability?” Journal of Strategic Information Systems 16 (3): 301–320. D’angelo, A., A. Majocchi, A. Zucchella, and T. Buck. 2013. “Geographical Pathways for SME Internationalization: Insights from an Italian Sample.” International Marketing Review 30 (2): 80–105. Dehlen, T., T. Zellweger, N. Kammerlander, and F. Halter. 2014. “The Role of Information Asymmetry in the Choice of Entrepreneurial Exit Routes.” Journal of Business Venturing 29 (2): 193–209. Denicolai, S., M. Ramirez, and J. Tidd. 2014. “Creating and Capturing Value from External Knowledge: The Moderating Role of Knowledge Intensity.” R&D Management 44 (3): 248–264. Dhanaraj, C., and P. W. Beamish. 2003. “A Resource-Based Approach to the Study of Export Performance.” Journal of Small Business Management 41 (3): 242–261. Edvinsson, L., and M. S. Malone. 1997. Intellectual Capital: Realizing Your Company’s True Value By Finding Its Hidden Brainpower. New York: Harper Business . Edvinsson, L., and M. S. Malone. 1997. Intellectual Capital: Realizing your Company’s True Value. Elsayed, K. 2006. “Reexamining the Expected Effect of Available Resources and Firm Size on Firm Environmental Orientation: An Empirical Study of UK Firms.” Journal of Business Ethics 65 (3): 297–308. Epstein, B., and A. Mirza. 2005. IAS 2005: Interpretation and Application of International Accounting and Financial Reporting Standards. Hoboken, NJ: Wiley. Fernhaber, S. A., P. P. Mcdougall-Covin, and D. A. Shepherd. 2009. “International Entrepreneurship: Leveraging Internal and External Knowledge Sources.” Strategic Entrepreneurship Journal 3 (4): 297–320. Gerpott, Torsten J., Sandra E. Thomas, and Alexander P. Hoffmann. 2008. “Intangible Asset Disclosure in the Telecommunications Industry.” Journal of Intellectual Capital 9 (1): 37–61. Gomez, J., and P. Vargas. 2012. “Intangible Resources and Technology Adoption in Manufacturing Firms.” Research Policy 41 (9): 1607–1619. Gopalakrishnan, S., and P. E. Bierly. 2006. “The Impact of Firm Size and Age on Knowledge Strategies During Product Development: A Study of the Drug Delivery Industry.” IEEE Transactions on Engineering Management 53 (1): 3–16. Granstrand, O., E. Bohlin, C. Oskarsson, and N. Sjoberg. 1992. “External Technology Acquisition in Large Multitechnology Corporations.” R & D Management 22 (2): 111–133. Gu, F., and W. M. Wang. 2005. “Intangible Assets, Information Complexity, and Analysts’ Earnings Forecasts.” Journal of Business Finance & Accounting 32 (9–10): 1673–1702. Hagen, B., A. Zucchella, P. Cerchiello, and N. De Giovanni. 2012. “International Strategy and Performance-Clustering Strategic Types of SMES.” International Business Review 21 (3): 369–382. Hall, R. 1992. “The Strategic Analysis of Intangible Resources.” Strategic Management Journal 13 (2): 135–144. Hall, R. 1993. “A Framework Linking Intangible Resources and Capabilities to Sustainable Competitive Advantage.” Strategic Management Journal 14 (8): 607–618. Hawkins, D. M. 1993. “Robustification of Cumulative Sum Charts by Winsorization.” Journal of Quality Technology 25 (4): 248–261. Hennart, J. F. 1991. “The Transaction Costs Theory of Joint Ventures – An Empirical Study of Japanese Subsidiaries in the United-States.” Management Science 37 (4): 483–497. Huang, H. C. 2011. “Technological Innovation Capability Creation Potential of Open Innovation: A Cross-Level Analysis in the Biotechnology Industry.” Technology Analysis & Strategic Management 23 (1): 49–63. Huarng, K. H., and T. H. K. Yu. 2011. “Entrepreneurship, Process Innovation and Value Creation by a Non-Profit SME.” Management Decision 49 (2): 284–296. Hussi, J., and G. Ahonen. 2002. “Managing Intangible Assets – A Question of Integration and Delicate Balance.” Journal of Intellectual Capital 3 (3): 277–286. Il Park, B., and P. N. Ghauri. 2011. “Key Factors Affecting Acquisition of Technological Capabilities from Foreign Acquiring Firms by Small and Medium Sized Local Firms.” Journal of World Business 46 (1): 116–125.



17

Jacobides, M. G., T. Knudsen, and M. Augier. 2006. “Benefiting From Innovation: Value Creation, Value Appropriation and the Role of Industry Architectures.” Research Policy 35 (8): 1200–1221. Jiang, C. X., Q. Yang, S. Li, and Y. Wang. 2011. “The Moderating Effect of Foreign Direct Investment Intensity on Local Firms’ Intangible Resources Investment and Performance Implications: A Case from China.” Journal of International Management 17 (4): 291–302. Jones, G. K., A. Lanctot, and H. J. Teegen. 2001. “Determinants and Performance Impacts of External Technology Acquisition.” Journal of Business Venturing 16 (3): 255–283. Kotlar, J., A. De Massis, F. Frattini, M. Bianchi, and H. Q. Fang. 2013. “Technology Acquisition in Family and Nonfamily Firms: A Longitudinal Analysis of Spanish Manufacturing Firms.” Journal of Product Innovation Management 30 (6): 1073–1088. Kristandl, G., and N. Bontis. 2007. “Constructing a Definition for Intangibles Using the Resource Based View of the Firm.” Management Decision 45 (9): 1510–1524. Leonardbarton, D. 1992. “Core Capabilities and Core Rigidities – A Paradox in Managing New Product Development.” Strategic Management Journal 13 (summer): 111–125. Li, Y. A., X. Y. Li, Y. Liu, and B. R. Barnes. 2011. “Knowledge Communication, Exploitation and Endogenous Innovation: The Moderating Effects of Internal Controls in SMES.” R & D Management 41 (2): 156–172. Maranto-Vargas, D., and R. G. T. Rangel. 2007. “Development of Internal Resources and Capabilities as Sources of Differentiation of SME Under Increased Global Competition: A Field Study in Mexico.” Technological Forecasting and Social Change 74 (1): 90–99. Mølbjerg-Jørgensen, K. 2006. “Conceptualising Intellectual Capital as a Language Game and Power.” Journal of Intellectual Capital 7 (1): 78–92. Narula, R. 2004. “R&D Collaboration by SMES: New Opportunities and Limitations in the Face of Globalisation.” Technovation 24 (2): 153–161. Nerkar, A., and S. Paruchuri. 2005. “Evolution of R&D Capabilities: The Role of Knowledge Networks Within a Firm.” Management Science 51 (5): 771–785. O’cass, A., and J. Weerawardena. 2010. “The Effects of Perceived Industry Competitive Intensity and Marketing-Related Capabilities: Drivers of Superior Brand Performance.” Industrial Marketing Management 39 (4): 571–581. Onyeiwu, S. 2003. “Some Determinants of Core Competencies: Evidence from a Binary-Logit Analysis.” Technology Analysis & Strategic Management 15 (1): 43–63. Pablo, A. L., S. B. Sitkin, and D. B. Jemison. 1996. “Acquisition Decision-Making Processes: The Central Role of Risk.” Journal of Management 22 (5): 723–746. Peteraf, M. A. 1993. “The Cornerstones of Competitive Advantage – A Resource-Based View.” Strategic Management Journal 14 (3): 179–191. Pike, S., G. Roos, and B. Marr. 2005. “Strategic Management of Intangible Assets and Value Drivers in R&D Organizations.” R & D Management 35 (2): 111–124. Powell, W. W., and K. Snellman. 2004. “The Knowledge Economy.” Annual Review of Sociology 30: 199–220. Rapp, A., K. J. Trainor, and R. Agnihotri. 2010. “Performance Implications of Customer-Linking Capabilities: Examining the Complementary Role of Customer Orientation and CRM Technology.” Journal of Business Research 63 (11): 1229–1236. Rumelt, R. P. 1984. “Towards a Strategic Theory of the Firm.” In Competitive Strategic Management, edited by B. Lr, 556–70. Englewood Cliffs, NJ: Prentice-Hall. Rumelt, R. P., D. Schendel, and D. J. Teece. 1991. “Strategic Management and Economics.” Strategic Management Journal 12: 5–29. Ruth, D., D. N. Iyer, and B. M. Sharp. 2013. “Motivation and Ability in the Decision to Acquire.” Journal of Business Research 66 (11): 2287–2293. Srivastava, A. 2014. “Why Have Measures of Earnings Quality Changed Over Time?” Journal of Accounting & Economics 57 (2–3): 196–217. Stanworth, J., C. Stanworth, A. Watson, D. Purdy, and S. Healeas. 2004. “Franchising as a Small Business Growth Strategy – A Resource-Based View of Organizational Development.” International Small Business Journal 22 (6): 539–559. Stolwijk, C. C. M., W. P. M. Vanhaverbeke, J. R. Ortt, M. W. Pieters, E. Den Hartigh, and C. Van Beers. 2012. “The Effect of Internal and External Technology Sourcing on Firm Performance Throughout the Technology Life Cycle.” Technology Analysis & Strategic Management 24 (10): 1013–1028. Surroca, J., J. A. Tribo, and S. Waddock. 2010. “Corporate Responsibility and Financial Performance: The Role of Intangible Resources.” Strategic Management Journal 31 (5): 463–490.


18

S. Denicolai et al.

Sveiby, K. E. 1997. The New Organisational Wealth – Managing and Measuring Knowledge-based Assets. San Francisco, CA: Berrett-Koehler. Taylor, P., and J. Lowe. 1997. “Are Functional Assets or Knowledge Assets the Basis of New Product Development Performance?” Technology Analysis & Strategic Management 9 (4): 473–488. Teece, D. J. 1998. “Capturing Value from Knowledge Assets: The New Economy, Markets for Know-How, and Intangible Assets.” California Management Review 40 (3): 55–79. Teece, D. J. 2006. “Reflections on ‘Profiting from Innovation’.” Research Policy 35 (8): 1131–1146. Thorpe, R., R. Holt, A. Macpherson, and L. Pittaway. 2005. “Using Knowledge Within Small and Medium-Sized Firms: A Systematic Review of the Evidence.” International Journal of Management Reviews 7 (4): 257–281. Troilo, G., L. M. De Luca, and K. Atuahene-Gima. 2014. “More Innovation with Less? A Strategic Contingency View of Slack Resources, Information Search, and Radical Innovation.” Journal of Product Innovation Management 31 (2): 259–277. Tsai, K. H., and J. C. Wang. 2007. “Inward Technology Licensing and Firm Performance: A Longitudinal Study.” R & D Management 37 (2): 151–160. Tsai, K. H., and J. C. Wang. 2008. “External Technology Acquisition and Firm Performance: A Longitudinal Study.” Journal of Business Venturing 23 (1): 91–112. Urde, M. 2009. “Uncovering the Corporate Brand’s Core Values.” Management Decision 47 (4): 616–638. Vega-Jurado, J., A. Gutierrez-Gracia, and I. Fernandez-De-Lucio. 2009. “Does External Knowledge Sourcing Matter for Innovation? Evidence from the Spanish Manufacturing Industry.” Industrial and Corporate Change 18 (4): 637–670. Wright, P. M., B. B. Dunford, and S. A. Snell. 2001. “Human Resources and the Resource Based View of the Firm.” Journal of Management 27 (6): 701–721.